Introduction to Data Management

Data management is the process of collecting, processing, storing, and reporting data to support the performance-based approach in air navigation systems. It ensures that decision-makers have access to accurate and meaningful information for optimizing performance. Key challenges include:

•    Data Collection: Ensuring data is collected from diverse sources with common definitions and standards.
•    Data Routing: Managing data flow across multiple organizations.
•    Data Sensitivity: Ensuring confidentiality of sensitive data.
•    Data Completeness: Achieving comprehensive data across time, geography, and classifications.
•    Granularity and Storage: Capturing detailed data at the source and managing growing data repositories.
•    Long-Term Monitoring: Maintaining continuity and stability in data collection for historical comparison.

Data management involves a set of interdependent functions, each with its own goals, activities, and responsibilities. There is a lot to keep track of, which is why it helps to have a framework to understand the data management comprehensively and see relationships between its component pieces.

The 11 elements of data management as highlighted in the DAMA-BoK (DMBoK - Data Management Body of Knowledge) are:

1.    Data Governance provides direction and oversight for data management by establishing a system of decision rights over data that accounts for the needs of the enterprise.
2.    Data Architecture defines the blueprint for managing data assets by aligning with organizational strategy to establish strategic data requirements and designs to meet these requirements.
3.    Data Modeling and Design is the process of discovering, analyzing, representing, and communicating data requirements in a precise form called the data model.
4.    Data Storage and Operations includes the design, implementation, and support of stored data to maximize its value. Operations provide support throughout the data lifecycle from planning for to disposal of data.
5.    Data Security ensures that data privacy and confidentiality are maintained, that data is not breached, and that data is accessed appropriately.
6.    Data Integration and Interoperability includes processes related to the movement and consolidation of data within and between data stores, applications, and organizations.
7.    Document and Content Management includes planning, implementation, and control activities used to manage the lifecycle of data and information found in a range of unstructured media, especially documents needed to support legal and regulatory compliance requirements.
8.     Reference and Master Data includes ongoing reconciliation and maintenance of core critical shared data to enable consistent use across systems of the most accurate, timely, and relevant version of truth about essential business entities.
9.     Data Warehousing and Business Intelligence includes the planning, implementation, and control processes to manage decision support data and to enable knowledge workers to get value from data via analysis and reporting.
10.     Metadata includes planning, implementation, and control activities to enable access to high quality, integrated Metadata, including definitions, models, data flows, and other information critical to understanding data and the systems through which it is created, maintained, and accessed.
11.     Data Quality includes the planning and implementation of quality management techniques to measure, assess, and improve the fitness of data for use within an organization.

As the performance management process matures, it will be necessary to incrementally improve the data quality and enhance the data collection processes. This section places significant emphasis on the practical aspects of (performance) data management. It is natural that this is not only discussed using terminology familiar to ATM community, but also requires the definition and use of some IT (Information Technology) terminologies.